An Object Model for Integrating Diverse Remote Sensing Satellite Sensors: A Case Study of Union Operation

Hu, Chuli; Li, Jia; Chen, Nengcheng; Guan, Qingfeng

doi:10.3390/rs6010677

Cited by 8 publications

(5 citation statements)

References 33 publications

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“…The integration of multi-sensor images in coherent time series and products becomes therefore of critical importance [1]. Besides, multi-sensor time series are a sensible solution to meet the requirements for agricultural monitoring, especially in areas affected by persistent cloud coverage at critical moments of the season [2,3].…”

Section: Introductionmentioning

confidence: 99%

Land Cover and Crop Type Classification along the Season Based on Biophysical Variables Retrieved from Multi-Sensor High-Resolution Time Series

Waldner

Lambert

et al. 2015

Remote Sensing

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Abstract:With the ever-increasing number of satellites and the availability of data free of charge, the integration of multi-sensor images in coherent time series offers new opportunities for land cover and crop type classification. This article investigates the potential of structural biophysical variables as common parameters to consistently combine multi-sensor time series and to exploit them for land/crop cover classification. Artificial neural networks were trained based on a radiative transfer model in order to retrieve high resolution LAI, FAPAR and FCOVER from Landsat-8 and SPOT-4. The correlation coefficients between field measurements and the retrieved biophysical variables were 0.83, 0.85 and 0.79 for LAI, FAPAR and FCOVER, respectively. The retrieved biophysical variables' time series displayed consistent average temporal trajectories, even though the class variability and signal-to-noise ratio increased compared to NDVI. Six random forest classifiers were trained and applied along the season with different inputs: spectral bands, NDVI, as well as FAPAR, LAI and FCOVER, separately Remote Sens. 2015, 7 10401 and jointly. Classifications with structural biophysical variables reached end-of-season overall accuracies ranging from 73%-76% when used alone and 77% when used jointly. This corresponds to 90% and 95% of the accuracy level achieved with the spectral bands and NDVI. FCOVER appears to be the most promising biophysical variable for classification. When assuming that the cropland extent is known, crop type classification reaches 89% with spectral information, 87% with the NDVI and 81%-84% with biophysical variables.

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Section: Introductionmentioning

confidence: 99%

Land Cover and Crop Type Classification along the Season Based on Biophysical Variables Retrieved from Multi-Sensor High-Resolution Time Series

Waldner

Lambert

et al. 2015

Remote Sensing

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“…The proposed object model (OM) considers every seafloor observatory sensor as an independent object, the attributes and operations of which fully cover the four categories of sensor information for sensor control logic as mentioned above. The object model is conceptually a set of sensor resource objects (SRO) designed with object-oriented methods, the basic notions of which are illustrated in Figure 2, such as composition and specialization [32]. Each SRO represents a particular type of sensor and the OM can be expressed in the following mathematical formula:…”

Section: Sensor Resource Objectmentioning

confidence: 99%

An Object Model for Seafloor Observatory Sensor Control in the East China Sea

2020

JMSE

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Seafloor observatories enable long-term, real-time, and continuous observation that marks a new way for oceanographic measurements. In terms of seafloor observatory research, sensor control is a key issue for the stable and effective operations of functional observatories. In this paper, an object model is proposed to standardize seafloor observatory sensor control and data acquisition. The object model is conceptionally designed as a set of sensor resource objects, based on the attributes and operations of which a client–server sensor control architecture is enabled for bidirectional information flow of control commands and observation data. The object model-based architecture is implemented with a prototype control system for plug-and-play enablement. The prototype system was put into a series of tests before applied to the East China Sea Experimental Seafloor Observatory, performing consistently with all the project requirements. Given the successful experiment, the object model design and prototype implementation are feasible to resolve seafloor observatory sensor control and beneficial for ocean observatory sciences.

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“…Previous studies presented approaches for integrating data from different active or passive sensors or a combination of both, * Corresponding author: thomas.zieher@oeaw.ac.at mainly to enhance the accuracy of classifiers (e.g. Solberg et al, 1994;Hu et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Sensor- And Scene-Guided Integration of TLS and Photogrammetric Point Clouds for Landslide Monitoring

Zieher

Toschi

Remondino

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Terrestrial and airborne 3D imaging sensors are well-suited data acquisition systems for the area-wide monitoring of landslide activity. State-of-the-art surveying techniques, such as terrestrial laser scanning (TLS) and photogrammetry based on unmanned aerial vehicle (UAV) imagery or terrestrial acquisitions have advantages and limitations associated with their individual measurement principles. In this study we present an integration approach for 3D point clouds derived from these techniques, aiming at improving the topographic representation of landslide features while enabling a more accurate assessment of landslide-induced changes. Four expert-based rules involving local morphometric features computed from eigenvectors, elevation and the agreement of the individual point clouds, are used to choose within voxels of selectable size which sensor’s data to keep. Based on the integrated point clouds, digital surface models and shaded reliefs are computed. Using an image correlation technique, displacement vectors are finally derived from the multi-temporal shaded reliefs. All results show comparable patterns of landslide movement rates and directions. However, depending on the applied integration rule, differences in spatial coverage and correlation strength emerge.

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An Object Model for Integrating Diverse Remote Sensing Satellite Sensors: A Case Study of Union Operation

Cited by 8 publications

References 33 publications

Land Cover and Crop Type Classification along the Season Based on Biophysical Variables Retrieved from Multi-Sensor High-Resolution Time Series

Land Cover and Crop Type Classification along the Season Based on Biophysical Variables Retrieved from Multi-Sensor High-Resolution Time Series

An Object Model for Seafloor Observatory Sensor Control in the East China Sea

Sensor- And Scene-Guided Integration of TLS and Photogrammetric Point Clouds for Landslide Monitoring

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